AANet: Attribute Attention Network for Person Re-Identifications
By Chiat-Pin Tay, Sharmili Roy, Kim-Hui Yap. Computer Vision and Pattern Recognition, 2019
332 citations recorded by the source corpus.
This paper proposes Attribute Attention Network (AANet), a new architecture that integrates person attributes and attribute attention maps into a classification framework to solve the person re-identification (re-ID) problem. Many person re-ID models typically employ semantic cues such as body parts or human pose to improve the re-ID performance. Attribute information, however, is often not utilized. The proposed AANet leverages on a baseline model that uses body parts and integrates the key attribute information in an unified learning framework. The AANet consists of a global person ID task, a part detection task and a crucial attribute detection task. By estimating the class responses of individual attributes and combining them to form the attribute attention map (AAM), a very strong discriminatory representation is constructed. The proposed AANet outperforms the best state-of-the-art method \cite{Sun_2018_ECCV} using ResNet-50 by 3.36\% in mAP and 3.12\% in Rank-1 accuracy on DukeMTMC-reID dataset. On Market1501 dataset, AANet achieves 92.38\% mAP and 95.10\% Rank-1 accuracy with re-ranking, outperforming~\cite{kalayeh2018human}, another state of the art method using ResNet-152, by 1.42\% in mAP and 0.47\% in Rank-1 accuracy. In addition, AANet can perform person attribute prediction (e.g., gender, hair length, clothing length etc.), and localize the attributes in the query image.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/cc10e4659cb72ef719414cdfc0e3c38a97f9c650
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Ling Shao
- Jianbing Shen
- Luc Van Gool
- Dacheng Tao
- Ming-Hsuan Yang
- David J. Crandall
- Wenguan Wang
- Jiebo Luo
Citations in the corpus
- Identity-Guided Human Semantic Parsing for Person Re-Identification (2020, background): “Many works employ extra semantic in terms of part/pose to locate body parts [6, 27, 29, 30, 32, 34, 40, 46, 49, 56] and try to achieve the pixel-level alignment.”
- FastReID: A Pytorch Toolbox for General Instance Re-identification (2020, unknown): “1 - AANet [16] (CVPR’19) 93.”
- Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification (2020, background): “Some work [22,26,41] has also investigated using multi-scale or different convolutional channels to capture the pixel-level/small-region-level attentions [35,36].”
- Infrared-Visible Cross-Modal Person Re-Identification with an X Modality (2020, background): “Encouraging performance has been observed, especially in the visible spectrum, where all the images are captured by visible cameras (Tay, Roy, and Yap 2019).”